The tractable cognition thesis

The recognition that human minds/brains are finite systems with limited resources for computation has led some researchers to advance the Tractable Cognition thesis : Human cognitive capacities are constrained by computational tractability. This thesis, if true, serves cognitive psychology by constraining the space of computational-level theories of cognition. To utilize this constraint, a precise and workable definition of "computational tractability" is needed. Following computer science tradition, many cognitive scientists and psychologists define computational tractability as polynomial-time computability, leading to the P-Cognition thesis . This article explains how and why the P-Cognition thesis may be overly restrictive, risking the exclusion of veridical computational-level theories from scientific investigation. An argument is made to replace the P-Cognition thesis by the FPT-Cognition thesis as an alternative formalization of the Tractable Cognition thesis (here, FPT stands for fixed-parameter tractable). Possible objections to the Tractable Cognition thesis, and its proposed formalization, are discussed, and existing misconceptions are clarified.     

A computational model of perception and action for cognitive robotics

Robots are increasingly expected to perform tasks in complex environments. To this end, engineers provide them with processing architectures that are based on models of human information processing. In contrast to traditional models, where information processing is typically set up in stages (i.e., from perception to cognition to action), it is increasingly acknowledged by psychologists and robot engineers that perception and action are parts of an interactive and integrated process. In this paper, we present HiTEC, a novel computational (cognitive) model that allows for direct interaction between perception and action as well as for cognitive control, demonstrated by task-related attentional influences. Simulation results show that key behavioral studies can be readily replicated. Three processing aspects of HiTEC are stressed for their importance for cognitive robotics: (1) ideomotor learning of action control, (2) the influence of task context and attention on perception, action planning, and learning, and (3) the interaction between perception and action planning. Implications for the design of cognitive robotics are discussed.     

Parameters, Predictions, and Evidence in Computational Modeling: A Statistical View Informed by ACT-R

Model validation in computational cognitive psychology often relies on methods drawn from the testing of theories in experimental physics. However, applications of these methods to computational models in typical cognitive experiments can hide multiple, plausible sources of variation arising from human participants and from stochastic cognitive theories, encouraging a "model fixed, data variable" paradigm that makes it difficult to interpret model predictions and to account for individual differences. This article proposes a likelihood-based, "data fixed, model variable" paradigm in which models are treated as stochastic processes in experiments with participant-to-participant variation that can be applied to a broad range of mechanistic cognitive architectures. This article discusses the implementation and implications of this view in model validation, with a concrete focus on a simple class of ACT-R models of cognition. This article is not intended as a recipe for broad application of these preliminary, proof-of-concept methods, but as a framework for communication between statisticians searching for interesting problems in the cognitive modeling sphere, and cognitive modelers interested in generalizing from deterministic to stochastic model validation, in the face of random variation in human experimental data.     

A computational cognitive framework of spatial memory in brains and robots

Computational cognitive models of spatial memory often neglect difficulties posed by the real world, such as sensory noise, uncertainty, and high spatial complexity. On the other hand, robotics is unconcerned with understanding biological cognition. Here, we describe a computational framework for robotic architectures aiming to function in realistic environments, as well as to be cognitively plausible.We motivate and describe several mechanisms towards achieving this despite the sensory noise and spatial complexity inherent in the physical world. We tackle error accumulation during path integration by means of Bayesian localization, and loop closing with sequential gradient descent. Finally, we outline a method for structuring spatial representations using metric learning and clustering. Crucially, unlike the algorithms of traditional robotics, we show that these mechanisms can be implemented in neuronal or cognitive models.We briefly outline a concrete implementation of the proposed framework as part of the LIDA cognitive architecture, and argue that this kind of probabilistic framework is well-suited for use in cognitive robotic architectures aiming to combine spatial functionality and psychological plausibility.     

Assessing Conversational Cognition Levels of Cognitive Theory and Associated Methodological Requirements

Despite the prominence of cognitively oriented research in interpersonal communication, relatively little is known about the interplay of cognitive and communicative processes during conversation. For interpersonal communication scholars or at least those interested in interaction, the study of cognition is most useful if it reveals how interactants produce, monitor, modify, and process messages while engaged in conversation. The failure to collect data about in-process conversational cognition is partially due to excessive reliance on what have been identified as “implementation level” cognitive theories and models. Such theories are primarily concerned with specifying cognitive “architectures” and provide little guidance about how cognition is adapted to conversational tasks. However, a dearth of methodological creativity is also partially to blame for the lack of data about in-process conversational cognition. On encountering the admittedly formidable methodological difficulties of collecting data about conversational cognition, researchers have tended to rely on traditional but inadequate methods, or worse, have simply ignored theoretical questions pertinent to cognition in-process. This article suggests that claims about cognition can be made at four levels—biological, implementation, algorithmic, and rational—but that theoretical claims about conversational cognition can most usefully be pursued at the rational and algorithmic levels. In an effort to promote more research at these levels, criteria for studying conversational cognition are proposed, and promising methods are reviewed.     

Through neural stimulation to behavior manipulation: a novel method for analyzing dynamical cognitive models

The dynamical systems' approach to cognition (Dynamicism) promises computational models that effectively embed cognitive processing within its more natural behavioral context. Dynamical cognitive models also pose difficult, analytical challenges, which motivate the development of new analytical methodology. We start by illustrating the challenge by applying two conventional analytical methods to a well-known Dynamicist model of categorical perception. We then introduce our own analysis, which works by analogy with neural stimulation methods, and which yields some novel insights into the way the model works. We then extend and apply the method to a second Dynamicist model, which captures the key psychophysical trends that emerge when humans and animals compare two numbers. The results of the analysis-which reveals units with tuning functions that are monotonically related to the magnitudes of the numbers that the agents must compare-offer a clear contribution to the contentious debate concerning the way number information is encoded in the brain.     

Emergence from brain architectures: a new cognitive science?

The way in which artificial intelligence has developed over the last 50 years has had a major role in shaping cognitive science as it is today. This has generated computational models of behaviour. The connectionist revival of the 1980s added a tinge of neurodynamics to this. Here I suggest that some post-connectionist work in artificial intelligence is turning towards an understanding and formalisation of the mechanisms of brain architectures which contribute to an emergence of cognition providing a closer link between brain mechanisms and experienced brain states. This even addresses the neurological basis of consciousness.     

Theoretical status of computational cognitive modeling

This article explores the view that computational models of cognition may constitute valid theories of cognition, often in the full sense of the term “theory”. In this discussion, this article examines various (existent or possible) positions on this issue and argues in favor of the view above. It also connects this issue with a number of other relevant issues, such as the general relationship between theory and data, the validation of models, and the practical benefits of computational modeling. All the discussions point to the position that computational cognitive models can be true theories of cognition.     

Cognitive neuroimaging: cognitive science out of the armchair

Cognitive scientists were not quick to embrace the functional neuroimaging technologies that emerged during the late 20th century. In this new century, cognitive scientists continue to question, not unreasonably, the relevance of functional neuroimaging investigations that fail to address questions of interest to cognitive science. However, some ultra-cognitive scientists assert that these experiments can never be of relevance to the study of cognition. Their reasoning reflects an adherence to a functionalist philosophy that arbitrarily and purposefully distinguishes mental information-processing systems from brain or brain-like operations. This article addresses whether data from properly conducted functional neuroimaging studies can inform and subsequently constrain the assumptions of theoretical cognitive models. The article commences with a focus upon the functionalist philosophy espoused by the ultra-cognitive scientists, contrasting it with the materialist philosophy that motivates both cognitive neuroimaging investigations and connectionist modelling of cognitive systems. Connectionism and cognitive neuroimaging share many features, including an emphasis on unified cognitive and neural models of systems that combine localist and distributed representations. The utility of designing cognitive neuroimaging studies to test (primarily) connectionist models of cognitive phenomena is illustrated using data from functional magnetic resonance imaging (fMRI) investigations of language production and episodic memory.     

A computational unification of cognitive behavior and emotion

Existing models that integrate emotion and cognition generally do not fully specify why cognition needs emotion and conversely why emotion needs cognition. In this paper, we present a unified computational model that combines an abstract cognitive theory of behavior control (PEACTIDM) and a detailed theory of emotion (based on an appraisal theory), integrated in a theory of cognitive architecture (Soar). The theory of cognitive control specifies a set of required computational functions and their abstract inputs and outputs, while the appraisal theory specifies in more detail the nature of these inputs and outputs and an ontology for their representation. We argue that there is a surprising functional symbiosis between these two independently motivated theories that leads to a deeper theoretical integration than has been previously obtained in other computational treatments of cognition and emotion. We use an implemented model in Soar to test the feasibility of the resulting integrated theory, and explore its implications and predictive power in several task domains.     

Criterial problems in creative cognition research

In creative cognition research, the Romantic view about creative cognition is traditionally rejected in favor of the modern view. The modern view about creative cognition maintains that creativity is neither mysterious nor unintelligible and that it is indeed susceptible to analysis. The paradigmatic objects of analysis in creative cognition research have been creative output and the creative process. The degree of creativity of an output is assessed in accordance with certain criterial definitions. The degree of creativity of a cognitive process is assessed in accordance with certain models of creative cognition, psychometric test measures, and neuroimaging studies that are grounded in certain criteria for assessment. The reliance on criterial definitions and criteria for assessment in analyzing either the creative output or the creative process suggests that creative cognition researchers remain under the sway of the classical, bundles-of-criteria theory of meaning. In this paper, I will critically evaluate the criterial problems that confront both criterial definitions and criteria for assessment before proposing an alternative theory of meaning.     

The extended cognition thesis: Its significance for the philosophy of (cognitive) science

While the extended cognition (EC) thesis has gained more followers in cognitive science and in the philosophy of mind and knowledge, our main goal is to discuss a different area of significance of the EC thesis: its relation to philosophy of science. In this introduction, we outline two major areas: (I) The role of the thesis for issues in the philosophy of cognitive science, such as: How do notions of EC figure in theories or research programs in cognitive science? Which versions of the EC thesis appear, and with which arguments to support them? (II) The potentials and limits of the EC thesis for topics in general philosophy of science, such as: Can naturalism perhaps be further advanced by means of the more recent EC thesis? Can we understand “big science” or laboratory research better by invoking some version of EC? And can the EC thesis help in overcoming the notorious cognitive/social divide in science studies?     

Self-directedness: A Process Approach to Cognition

Standard approaches to cognition emphasise structures (representations and rules) much more than processes, in part because this appears to be necessary to capture the normative features of cognition. However the resultant models are inflexible and face the problem of computational intractability. I argue that the ability of real world cognition to cope with complexity results from deep and subtle coupling between cognitive and non-cognitive processes. In order to capture this, theories of cognition must shift from a structural rule-defined conception of cognition to a thoroughgoing embedded process approach.     

Cognitive functions and corticostriatal circuits: insights from Huntington's disease

The basic mechanisms of information processing by corticostriatal circuits are currently a matter of intense debate amongst cognitive scientists. Huntington's disease, an autosomal-dominant neurogenetic disorder characterized clinically by a triad of motor, cognitive, and affective disturbance, is associated with neuronal loss within corticostriatal circuits, and as such provides a valuable model for understanding the role of these circuits in normal behaviour, and their disruption in disease. We review findings from our studies of the breakdown of cognition in Huntington's disease, with a particular emphasis on executive functions and visual recognition memory. We show that Huntington's disease patients exhibit a neuropsychological profile that shows a discernible pattern of progression with advancing disease, and appears to result from a breakdown in the mechanisms of response selection. These findings are consistent with recent computational models that suggest that corticostriatal circuits compute the patterns of sensory input and response output which are of behavioural significance within a particular environmental context.     

Just because you're imaging the brain doesn't mean you can stop using your head: a primer and set of first principles

Developments within the neurosciences, cognitive sciences, and social sciences have contributed to the emergence of social neuroscience. Among the most obvious contemporary developments are brain-imaging procedures such as functional magnetic resonance imaging. The authors outline a set of first principles designed to help make sense of brain-imaging research within the fields of cognitive and social neuroscience. They begin with a principle few would debate--that social cognition, emotion, and behavior involve the brain--but whose implications might not be entirely obvious to those new to the field. The authors conclude that (a). complex aspects of the mind and behavior will benefit from yet a broader collaboration of neuroscientists, cognitive scientists, and social scientists, and (b). social psychologists bring important theoretical, methodological, and statistical expertise to this interdisciplinary enterprise.     

Integrating a cognitive computational model of planning and decision-making considering affective information

Planning and decision-making are two of the cognitive functions involved in the solution of problems. These functions, among others, have been studied from the point of view of a new field known as cognitive informatics focused on the development of cognitive architectures, autonomous agents, and human robots that are capable of showing human-like behavior. We present an exhaustive study of current biological and computational models proposed in the fields of neuroscience, psychology, and cognitive informatics. Also, we present a deep review of the brain areas involved in planning, decision-making, and affection. However, the majority of the proposed computational models are seeking to mimic human external behavior. This paper aims to contribute to the cognitive informatics field with an innovative cognitive computational model of planning and decision-making. The two main differences of our model with respect to the current models in the literature are: (i) our model considers affective and motivational information as a basic and essential trigger in planning and decision-making processes; (ii) our model attempts to mimic both the internal human brain as well as the external human behavior. We developed a computational model capable of offering a direct mapping from human brain areas to computational modules of our model. Thus, in this paper we present our model from a conceptual, formal, and computational approach in order to show how our proposal must be implemented. Finally, a set of tests were conducted in order to validate our proposal. These tests show an interesting comparison between the behavior of our prototype and the behavior exhibited by some people involved in a case study.     

Parameter Inference for Computational Cognitive Models with Approximate Bayesian Computation

This paper addresses a common challenge with computational cognitive models: identifying parameter values that are both theoretically plausible and generate predictions that match well with empirical data. While computational models can offer deep explanations of cognition, they are computationally complex and often out of reach of traditional parameter fitting methods. Weak methodology may lead to premature rejection of valid models or to acceptance of models that might otherwise be falsified. Mathematically robust fitting methods are, therefore, essential to the progress of computational modeling in cognitive science. In this article, we investigate the capability and role of modern fitting methods—including Bayesian optimization and approximate Bayesian computation—and contrast them to some more commonly used methods: grid search and Nelder–Mead optimization. Our investigation consists of a reanalysis of the fitting of two previous computational models: an Adaptive Control of Thought—Rational model of skill acquisition and a computational rationality model of visual search. The results contrast the efficiency and informativeness of the methods. A key advantage of the Bayesian methods is the ability to estimate the uncertainty of fitted parameter values. We conclude that approximate Bayesian computation is (a) efficient, (b) informative, and (c) offers a path to reproducible results.     

生成认知：理论基础与实践走向

生成认知是具身认知思潮中的一个新取向。它主张认知是通过身体活动“生成的” (enacted)。认知的起点不是一个怎样精确表征世界的信息加工问题, 而是行动者在情境中怎样利用知觉来指导自己的行动。认知不是通过精确的心理表征“恢复”世界, 而是通过知觉引导的行动“生成”或“造就”一个自己的世界。认知是具身的行动, 认知结构形成于经常和反复出现的感觉运动模式, 与身体构造和身体活动具有深刻连续性。梅洛·庞蒂的身体现象学对生成认知具有深刻影响。同时, 詹姆斯、杜威等人的实用主义哲学对于实践行动的强调也深刻影响了生成认知。这种认知观强调了“行动”对心智的意义, 引发许多争议, 也促进了心理学研究范式的转变。     

Computational enactivism under the free energy principle

In this paper, I argue that enactivism and computationalism—two seemingly incompatible research traditions in modern cognitive science—can be fruitfully reconciled under the framework of the free energy principle (FEP). FEP holds that cognitive systems encode generative models of their niches and cognition can be understood in terms of minimizing the free energy of these models. There are two philosophical interpretations of this picture. A computationalist will argue that as FEP claims that Bayesian inference underpins both perception and action, it entails a concept of cognition as a computational process. An enactivist, on the other hand, will point out that FEP explains cognitive systems as constantly self-organizing to non-equilibrium steady-state. My claim is that these two interpretations are both true at the same time and that they enlighten each other.